{"type":"ScholarlyArticle","title":"Learning for Graph Matching and Related Combinatorial Optimization Problems","authors":[{"name":"Junchi Yan","slug":"junchi-yan","given":"Junchi","family":"Yan","affiliation":"Department of CSE , MoE Key Lab of Artificial Intelligence , Shanghai Jiao Tong University"},{"name":"Shuang Yang","slug":"shuang-yang","given":"Shuang","family":"Yang","affiliation":"Ant Financial Services Group"},{"name":"Edwin Hancock","slug":"edwin-hancock","given":"Edwin","family":"Hancock","affiliation":"Department of Computer Science , University of York"}],"abstract":"This survey gives a selective review of recent development of machine learning (ML) for combinatorial optimization (CO), especially for graph matching. The synergy of these two well-developed areas (ML and CO) can potentially give transformative change to artificial intelligence, whose foundation relates to these two building blocks. For its representativeness and wide-applicability, this paper is more focused on the problem of weighted graph matching, especially from the learning perspective. For graph matching, we show that many learning techniques e.g. convolutional neural networks, graph neural networks, reinforcement learning can be effectively incorporated in the paradigm for extracting the node features, graph structure features, and even the matching engine. We further present outlook for the new settings for learning graph matching, and direction towards more integrated combinatorial optimization solvers with prediction models, and also the mutual embrace of traditional solver and machine learning components.","doi":"10.24963/ijcai.2020/694","url":"https://ijcai.org/proceedings/2020/694","pdf":"https://ijcai.org/proceedings/2020/0694.pdf","markdown":"https://ijcai.org/proceedings/2020/694.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/694","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Survey track","track":"Survey track","number":694,"pages":{"first":4988,"last":4996},"keywords":["Machine Learning: general","Planning and Scheduling: general","Constraints and Satisfiability: general"],"videos":[],"references":[{"position":1,"raw":"[Adamczewski et al., 2015] K. Adamczewski, Y. Suh, and K. Lee. Discrete tabu search for graph matching. In ICCV, 2015.","title":"Discrete tabu search for graph matching","authors":"K Adamczewski, Y Suh, K Lee","year":"2015","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Adams and Zemel, 2011] Ryan Prescott Adams and Richard S Zemel. Ranking via sinkhorn propagation. arXiv:1106.1925, 2011.","title":"Ranking via sinkhorn propagation","authors":"Ryan Prescott, Adams, Richard S Zemel","year":"2011","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Balcan et al., 2018] Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, and Ellen Vitercik. Learning to branch. In ICML, 2018.","title":"Learning to branch","authors":"Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, Ellen Vitercik","year":"2018","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bello et al., 2016] I.","title":null,"authors":"Bello","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"Bello, H. Pham, Q. V Le, M. Norouzi, and S. Bengio. Neural combinatorial optimization with reinforcement learning. arXiv preprint arXiv:1611.09940, 2016.","title":"Neural combinatorial optimization with reinforcement learning","authors":"H Bello, Q Pham, M Le, S Norouzi, Bengio","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Bengio et al., 2018] Y. Bengio, A. Lodi, and A. Prouvost. Machine learning for combinatorial optimization: a methodological tour d'horizon. arXiv preprint arXiv:1811.06128, 2018.","title":"Machine learning for combinatorial optimization: a methodological tour d'horizon","authors":"Y Bengio, A Lodi, A Prouvost","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Bertsimas and Stellato, 2019] Dimitris Bertsimas and Bartolomeo Stellato. Online mixed-integer optimization in milliseconds. arXiv preprint arXiv:1907.02206, 2019.","title":"Online mixed-integer optimization in milliseconds","authors":"Dimitris Bertsimas, Bartolomeo Stellato","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Caetano et al., 2009] T. Caetano, J. McAuley, L. Cheng, Q. Le, and A. J. Smola. Learning graph matching. TPAMI, 31(6):1048- 1058, 2009.","title":"Learning graph matching","authors":"T Caetano, J Mcauley, L Cheng, Q Le, A J Smola","year":"2009","venue":"TPAMI","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Cao and Yu, 2016] X. Cao and Y. Yu. Bass: A bootstrapping ap- proach for aligning heterogenous social networks. In Joint Euro- pean Conference on Machine Learning and Knowledge Discov- ery in Databases, 2016.","title":"Bass: A bootstrapping approach for aligning heterogenous social networks","authors":"Yu ; Cao, X Cao, Y Yu","year":"2016","venue":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Chen et al., 2014] Y. Chen, L. Guibas, and Q. Huang. Near- optimal joint object matching via convex relaxation. In ICML, 2014.","title":"Nearoptimal joint object matching via convex relaxation","authors":"Y Chen, L Guibas, Q Huang","year":"2014","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Chertok and Keller, 2010] M. Chertok and Y. Keller. Efficient high order matching. TPAMI, 2010.","title":"Efficient high order matching","authors":"M Chertok, Y Keller","year":"2010","venue":"TPAMI","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Cho et al., 2010] M. Cho, J. Lee, and K. M. Lee. Reweighted ran- dom walks for graph matching. In ECCV, 2010.","title":"Reweighted random walks for graph matching","authors":"M Cho, J Lee, K M Lee","year":"2010","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Cho et al., 2013] M. Cho, K. Alahari, and J. Ponce. Learning graphs to match. In ICCV, 2013.","title":"Learning graphs to match","authors":"M Cho, K Alahari, J Ponce","year":"2013","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Cour et al., 2006] T. Cour, P. Srinivasan, and J. Shi. Balanced graph matching. In NIPS, 2006.","title":"Balanced graph matching","authors":"T Cour, P Srinivasan, J Shi","year":"2006","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Dohi et al., 2007] Tadashi Dohi, Shunji Osaki, and Katsushige Sawaki. Recent advances in stochastic operations research. 2007.","title":"Recent advances in stochastic operations research","authors":"Tadashi Dohi, Shunji Osaki, Katsushige Sawaki","year":"2007","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Du and Tong, 2019] B. Du and H. Tong. Mrmine: Multi-resolution multi-network embedding. In CIKM, 2019.","title":"Mrmine: Multi-resolution multi-network embedding","authors":"B Du, H Tong","year":"2019","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Du et al., 2019] X. Du, J. Yan, and H. Zha. Joint link prediction and network alignment via cross-graph embedding. In IJCAI, 2019.","title":"Joint link prediction and network alignment via cross-graph embedding","authors":"Du","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2019/312"},{"position":18,"raw":"[Duchenne et al., 2011] O. Duchenne, F. Bach, I. Kweon, and J. Ponce. A tensor-based algor ithm for high-order graph match- ing. PAMI, 2011.","title":"A tensor-based algor ithm for high-order graph matching","authors":"O Duchenne, F Bach, I Kweon, J Ponce","year":"2011","venue":"PAMI","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Fey et al., 2020] M. Fey, J. E Lenssen, C. Morris, J. Masci, and N. M Kriege. Deep graph matching consensus. In ICLR, 2020.","title":"Deep graph matching consensus","authors":"M Fey, J Lenssen, C Morris, J Masci, N M Kriege","year":"2020","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Finn et al., 2017] Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep net- works. In Proceedings of the 34th ICML-Volume 70, pages 1126- 1135. JMLR. org, 2017.","title":"Model-agnostic meta-learning for fast adaptation of deep networks","authors":"Finn","year":"2017","venue":"Proceedings of the 34th ICML","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Gasse et al., 2019] M. Gasse, D. Chételat, N. Ferroni, L. Charlin, and A. Lodi. Exact combinatorial optimization with graph con- volutional neural networks. In NeurIPS, 2019.","title":"Exact combinatorial optimization with graph convolutional neural networks","authors":"M Gasse, D Chételat, N Ferroni, L Charlin, A Lodi","year":"2019","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Gold and Rangarajan, 1996] S. Gold and A. Rangarajan. A gradu- ated assignment algorithm for graph matching. TPAMI, 1996.","title":"A graduated assignment algorithm for graph matching","authors":"S Gold, A Rangarajan","year":"1996","venue":"TPAMI","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Guibas et al., 2019] L. Guibas, Q. Huang, and Z. Liang. A condi- tion number for joint optimization of cycle-consistent networks. In NIPS, 2019.","title":"A condition number for joint optimization of cycle-consistent networks","authors":"L Guibas, Q Huang, Z Liang","year":"2019","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Heimann et al., 2018] M. Heimann, H. Shen, T. Safavi, and D. Koutra. Regal: Representation learning-based graph align- ment. In CIKM, 2018.","title":"Regal: Representation learning-based graph alignment","authors":"M Heimann, H Shen, T Safavi, D Koutra","year":"2018","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Hu et al., 2018] N. Hu, Q. Huang, B. Thibert, and L. Guibas. Dis- tributable consistent multi-graph matching. CVPR, 2018.","title":"Distributable consistent multi-graph matching","authors":"N Hu, Q Huang, B Thibert, L Guibas","year":"2018","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Huang and Guibas, 2013] Q. Huang and L. Guibas. Consistent shape maps via semidefinite programming. In SGP, 2013.","title":"Consistent shape maps via semidefinite programming","authors":"Q Huang, L Guibas","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Huang et al., 2019] Jiayi Huang, Mostofa Patwary, and Gre- gory Diamos. Coloring big graphs with alphagozero. arXiv:1902.10162, 2019.","title":"Coloring big graphs with alphagozero","authors":"Huang","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Jiang et al., 2019a] B. Jiang, P. Sun, J. Tang, and B. Luo. Glmnet: Graph learning-matching networks for feature matching. arXiv preprint arXiv:1911.07681, 2019.","title":"Glmnet: Graph learning-matching networks for feature matching","authors":"Jiang","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Jiang et al., 2019b] M. Jiang, Z. Pan T. Hai, H. Wang, Y. Jia, and C. Deng. Multi-agent deep reinforcement learning for multi- object tracker. IEEE ACCESS, 2019.","title":"Multi-agent deep reinforcement learning for multiobject tracker","authors":"Jiang","year":"2019","venue":"IEEE ACCESS","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Khalil et al., 2017] E. Khalil, H. Dai, Y. Zhang, B. Dilkina, and L. Song. Learning combinatorial optimization algorithms over graphs. In NIPS, 2017.","title":"Learning combinatorial optimization algorithms over graphs","authors":"E Khalil, H Dai, Y Zhang, B Dilkina, L Song","year":"2017","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Kim et al., 2012] V. G. Kim, W. Li, N. J. Mitra, S. DiVerdi, and T. Funkhouser. Exploring collections of 3d models using fuzzy correspondences. In SIGGRAPH, 2012.","title":"Exploring collections of 3d models using fuzzy correspondences","authors":"V G Kim, W Li, N J Mitra, S Diverdi, T Funkhouser","year":"2012","venue":"SIGGRAPH","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Kong et al., 2013] X. Kong, J. Zhang, and P. S Yu. Inferring an- chor links across multiple heterogeneous social networks. In CIKM, 2013.","title":"Inferring anchor links across multiple heterogeneous social networks","authors":"Kong","year":"2013","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Kool et al., 2018] Wouter Kool, Herke van Hoof, and Max Welling. Attention, learn to solve routing problems! In ICLR, 2018.","title":"Attention, learn to solve routing problems! In ICLR","authors":"Wouter Kool, Herke Van Hoof, Max Welling","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Kosowsky and Yuille, 1994] Jeffrey J Kosowsky and Alan L Yuille. The invisible hand algorithm: Solving the assignment problem with statistical physics. Neural networks, 7(3):477-490, 1994.","title":"The invisible hand algorithm: Solving the assignment problem with statistical physics","authors":"J Jeffrey, Alan L Kosowsky, Yuille","year":"1994","venue":"Neural networks","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"Kuhn, 1955","title":null,"authors":"Kuhn","year":"1955","venue":null,"doi":null,"url":null,"internal_url":null},{"position":36,"raw":"H. W. Kuhn. The hungarian method for the assign- ment problem. In Export. Naval Research Logistics Quarterly, pages 83-97, 1955.","title":"The hungarian method for the assignment problem","authors":"H W Kuhn","year":"1955","venue":"Export. Naval Research Logistics Quarterly","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Lee et al., 2010] J. Lee, M. Cho, and K.M. Lee. A graph matching algorithm using data-driven markov chain monte carlo sampling. In ICPR, 2010.","title":"A graph matching algorithm using data-driven markov chain monte carlo sampling","authors":"J Lee, M Cho, K M Lee","year":"2010","venue":"ICPR","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Lee et al., 2011] J. Lee, M. Cho, and K. M. Lee. Hyper-graph matching via reweighted randomwalks. In CVPR, 2011.","title":"Hyper-graph matching via reweighted randomwalks","authors":"J Lee, M Cho, K M Lee","year":"2011","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Leonardos et al., 2017] S. Leonardos, X. Zhou, and K. Daniilidis. Distributed consistent data association. ICRA, 2017.","title":"Distributed consistent data association","authors":"S Leonardos, X Zhou, K Daniilidis","year":"2017","venue":"ICRA","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Leordeanu and Hebert, 2005] M. Leordeanu and M. Hebert. A spectral technique for correspondence problems using pairwise constraints. In ICCV, 2005.","title":"A spectral technique for correspondence problems using pairwise constraints","authors":"M Leordeanu, M Hebert","year":"2005","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Leordeanu et al., 2009] M. Leordeanu, M. Hebert, and R Suk- thankar. An integer projected fixed point method for graph matching and map inference. In NIPS, 2009.","title":"An integer projected fixed point method for graph matching and map inference","authors":"M Leordeanu, M Hebert, Sukthankar","year":"2009","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Leordeanu et al., 2011] M. Leordeanu, A. Zanfir, and C. Smin- chisescu. Semi-supervised learning and optimization for hyper- graph matching. In ICCV, 2011.","title":"Sminchisescu. Semi-supervised learning and optimization for hypergraph matching","authors":"M Leordeanu, A Zanfir, C","year":"2011","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Leordeanu et al., 2012] M. Leordeanu, R. Sukthankar, and M. Hebert. Unsupervised learning for graph matching. Int. J. Comput. Vis., pages 28-45, 2012.","title":"Unsupervised learning for graph matching","authors":"M Leordeanu, R Sukthankar, M Hebert","year":"2012","venue":"Int. J. Comput. Vis","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Li et al., 2016] L. Li, W. K. Cheun, L. Xin, and L. Liao. Aligning users across social networks using network embedding. In IJCAI, 2016.","title":"Aligning users across social networks using network embedding","authors":"L Li, W K Cheun, L Xin, L Liao","year":"2016","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2016/254"},{"position":45,"raw":"[Li et al., 2019] Yujia Li, Chenjie Gu, Thomas Dullien, Oriol Vinyals, and Pushmeet Kohli. Graph matching networks for learning the similarity of graph structured objects. In ICML, pages 3835-3845, 2019.","title":"Graph matching networks for learning the similarity of graph structured objects","authors":"Yujia Li, Chenjie Gu, Thomas Dullien, Oriol Vinyals, Pushmeet Kohli","year":"2019","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Liu et al., 2016] Li Liu, William K Cheung, Xin Li, and Lejian Liao. Aligning users across social networks using network em- bedding. In IJCAI, 2016.","title":"Aligning users across social networks using network embedding","authors":"Li Liu, William K Cheung, Xin Li, Lejian Liao","year":"2016","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2016/254"},{"position":47,"raw":"[Liu et al., 2020] Yan-li Liu, Chu-min Li, Hua Jiang, and Kun He. A learning based branch and bound for maximum common sub- graph problems. In AAAI, 2020.","title":"A learning based branch and bound for maximum common subgraph problems","authors":"Yan-Li Liu, Chu-Min Li, Hua Jiang, Kun He","year":"2020","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Liu, 2018] X. Liu. Graph matching by graph nerual network. Mas- ter Thesis, University of Illinois at Urbana-Champaign, 2018.","title":"Graph matching by graph nerual network","authors":"X Liu","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Loiola et al., 2007] E. M. Loiola, N. M. de Abreu, P. O. Boaventura-Netto, P. Hahn, and T. Querido. A survey for the quadratic assignment problem. EJOR, 2007.","title":"A survey for the quadratic assignment problem","authors":"E M Loiola, N M De Abreu, P O Boaventura-Netto, P Hahn, T Querido","year":"2007","venue":null,"doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Lombardi and Milano, 2018] M. Lombardi and M. Milano. Boost- ing combinatorial problem modeling with machine learning. In IJCAI, 2018.","title":"Boosting combinatorial problem modeling with machine learning","authors":"M Lombardi, M Milano","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/772"},{"position":51,"raw":"[Man et al., 2016] T. Man, H. Shen, S. Liu, X. Jin, and X. Cheng. Predict anchor links across social networks via an embedding ap- proach. In IJCAI, 2016.","title":"Predict anchor links across social networks via an embedding approach","authors":"T Man, H Shen, S Liu, X Jin, X Cheng","year":"2016","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2016/261"},{"position":52,"raw":"[Ngoc et al., 2015] Q. Ngoc, A. Gautier, and M. Hein. A flexible tensor block coo rd inate ascent scheme for hypergraph matching. In CVPR, 2015.","title":"A flexible tensor block coo rd inate ascent scheme for hypergraph matching","authors":"Q Ngoc, A Gautier, M Hein","year":"2015","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":53,"raw":"[Nowak et al., 2018] A. Nowak, S. Villar, A. Bandeira, and J. Bruna. Revised note on learning quadratic assignment with graph neural networks. In DSW, 2018.","title":"Revised note on learning quadratic assignment with graph neural networks","authors":"A Nowak, S Villar, A Bandeira, J Bruna","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":54,"raw":"[Pachauri et al., 2013] D. Pachauri, R. Kondor, and S. Vikas. Solv- ing the multi-way matching problem by permutation synchro- nization. In NIPS, 2013.","title":"Solving the multi-way matching problem by permutation synchronization","authors":"D Pachauri, R Kondor, S Vikas","year":"2013","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":55,"raw":"[Perozzi et al., 2014] Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 701-710. ACM, 2014.","title":"Deepwalk: Online learning of social representations","authors":"Bryan Perozzi, Rami Al-Rfou, Steven Skiena","year":"2014","venue":"Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":56,"raw":"[Prates et al., 2019] M. Prates, P. Avelar, H. Lemos, L. Lamb, and M. Vardi. Learning to solve np-complete problems: A graph neural network for decision tsp. In AAAI, 2019.","title":"Learning to solve np-complete problems: A graph neural network for decision tsp","authors":"M Prates, P Avelar, H Lemos, L Lamb, M Vardi","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":57,"raw":"[Ren et al., 2018] L. Ren, X. Yuan, J. Lu, M. Yang, and J. Zhou. Deep reinforcement learning with iterative shift for visual track- ing. In ECCV, 2018.","title":"Deep reinforcement learning with iterative shift for visual tracking","authors":"L Ren, X Yuan, J Lu, M Yang, J Zhou","year":"2018","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":58,"raw":"[Ribeiro et al., 2017] Leonardo FR Ribeiro, Pedro HP Saverese, and Daniel R Figueiredo. struc2vec: Learning node represen- tations from structural identity. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 385-394. ACM, 2017.","title":"Daniel R Figueiredo. struc2vec: Learning node representations from structural identity","authors":"Ribeiro","year":"2017","venue":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":59,"raw":"[Rosello and Kochenderfer, 2018] P. Rosello and M. J. Kochender- fer. Multi-agent reinforcement learning for multi-object tracking. In AAMAS, 2018.","title":"Multi-agent reinforcement learning for multi-object tracking","authors":"P Rosello, M J Kochenderfer","year":"2018","venue":"AAMAS","doi":null,"url":null,"internal_url":null},{"position":60,"raw":"[Schellewald and Schnörr, 2005] C. Schellewald and C. Schnörr. Probabilistic subgraph matching based on convex relaxation. In EMMCVPR, 2005.","title":"Probabilistic subgraph matching based on convex relaxation","authors":"C Schellewald, C Schnörr","year":"2005","venue":"EMMCVPR","doi":null,"url":null,"internal_url":null},{"position":61,"raw":"[Sghir et al., 2018] Ines Sghir, Ines Ben Jaafar, and Khaled Ghédira. A multi-agent based optimization method for combina- torial optimization problems. International Journal on Artificial Intelligence Tools, 27(05):1850021, 2018.","title":"A multi-agent based optimization method for combinatorial optimization problems","authors":"Ines Sghir, Ines Ben Jaafar, Khaled Ghédira","year":"2018","venue":"International Journal on Artificial Intelligence Tools","doi":null,"url":null,"internal_url":null},{"position":62,"raw":"[Shen et al., 2019] Y. Shen, Y. Shi, J. Zhang, and K. B Letaief. Lorm: Learning to optimize for resource management in wire- less networks with few training samples. IEEE Transactions on Wireless Communications, 2019.","title":"Lorm: Learning to optimize for resource management in wireless networks with few training samples","authors":"Y Shen, Y Shi, J Zhang, K B Letaief","year":"2019","venue":"IEEE Transactions on Wireless Communications","doi":null,"url":null,"internal_url":null},{"position":63,"raw":"[Suh et al., 2012] Y. Suh, M. Cho, and K. M. Lee. Graph matching via sequential monte carlo. In ECCV, 2012.","title":"Graph matching via sequential monte carlo","authors":"Y Suh, M Cho, K M Lee","year":"2012","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":64,"raw":"[Tan et al., 2014] S. Tan, Z. Guan, D. Cai, X. Qin, J. Bu, and C. Chen. Mapping users across networks by manifold alignment on hypergraph. In AAAI, 2014.","title":"Mapping users across networks by manifold alignment on hypergraph","authors":"S Tan, Z Guan, D Cai, X Qin, J Bu, C Chen","year":"2014","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":65,"raw":"[Torr, 2003] P. H. S. Torr. Solving markov random fields using semidefinite programmin. In AISTATS, 2003.","title":"Solving markov random fields using semidefinite programmin","authors":"P H S Torr","year":"2003","venue":"AISTATS","doi":null,"url":null,"internal_url":null},{"position":66,"raw":"[Torresani et al., 2008] L. Torresani, V. Kolmogorov, and C. Rother. Feature correspondence via graph matching: Models and global optimization. In ECCV, 2008.","title":"Feature correspondence via graph matching: Models and global optimization","authors":"L Torresani, V Kolmogorov, C Rother","year":"2008","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":67,"raw":"[Vinyals et al., 2015] O.l Vinyals, M. Fortunato, and N. Jaitly. Pointer networks. In NIPS, 2015. [Vlastelica et al., 2020]","title":"Pointer networks","authors":"O Vinyals, M Fortunato, N Jaitly","year":"2015","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":68,"raw":"M. Vlastelica, A. Paulus, V. Musil, G. Mar- tius, and M. Rolínek. Differentiation of blackbox combinatorial solvers. 2020.","title":"Differentiation of blackbox combinatorial solvers","authors":"M Vlastelica, A Paulus, V Musil, G Martius, M Rolínek","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":69,"raw":"[Wang et al., 2018] Q. Wang, X. Zhou, and K. Daniilidis. Multi- image semantic matching by mining consistent features. In CVPR, 2018.","title":"Multiimage semantic matching by mining consistent features","authors":"Q Wang, X Zhou, K Daniilidis","year":"2018","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":70,"raw":"[Wang et al., 2019a] R. Wang, J. Yan, and X. Yang. Learning com- binatorial embedding networks for deep graph matching. In ICCV, 2019.","title":"Learning combinatorial embedding networks for deep graph matching","authors":"Wang","year":"2019","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":71,"raw":"[Wang et al., 2019b] R. Wang, Ju. Yan, and X. Yang. Neural graph matching network: Learning lawlers quadratic assignment prob- lem with extension to hypergraph and multiple-graph matching. arXiv preprint arXiv:1911.11308, 2019.","title":"Neural graph matching network: Learning lawlers quadratic assignment problem with extension to hypergraph and multiple-graph matching","authors":"Wang","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":72,"raw":"[Wang et al., 2019c] Y. Wang, Y. Tong, C. Long, P. Xu, K. Xu, and W. Lv. Adaptive dynamic bipartite graph matching: A reinforce- ment learning approach. In ICDE, 2019.","title":"Adaptive dynamic bipartite graph matching: A reinforcement learning approach","authors":"Wang","year":"2019","venue":"ICDE","doi":null,"url":null,"internal_url":null},{"position":73,"raw":"[Wang et al., 2019d] Yansheng Wang, Yongxin Tong, Cheng Long, Pan Xu, Ke Xu, and Weifeng Lv. Adaptive dynamic bipartite graph matching: A reinforcement learning approach. In ICDE, pages 1478-1489. IEEE, 2019.","title":"Adaptive dynamic bipartite graph matching: A reinforcement learning approach","authors":"Wang","year":"2019","venue":"ICDE","doi":null,"url":null,"internal_url":null},{"position":74,"raw":"[Wang et al., 2020] T. Wang, Z. Jiang, and J. Yan. Clustering-aware multiple graph matching via decayed pairwise matching compo- sition. In AAAI, 2020.","title":"Clustering-aware multiple graph matching via decayed pairwise matching composition","authors":"T Wang, Z Jiang, J Yan","year":"2020","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":75,"raw":"[Xu et al., 2019] H. Xu, D. Luo, H. Zha, and L. Carin. Gromov- wasserstein learning for graph matching and node embedding. ICML, 2019.","title":"Gromovwasserstein learning for graph matching and node embedding","authors":"H Xu, D Luo, H Zha, L Carin","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":76,"raw":"[Yan et al., 2015a] J. Yan, J. Wang, H. Zha, X. Yang, and S. Chu. Consistency-driven alternating optimization for multi- graph matching: A unified approach. IEEE Transactions on Im- age Processing, 24(3):994-1009, 2015.","title":"Consistency-driven alternating optimization for multigraph matching: A unified approach","authors":"Yan","year":"2015","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":77,"raw":"[Yan et al., 2015b] J. Yan, C. Zhang, H. Zha, W. Liu, X. Yang, and S. Chu. Discrete hyper-graph matching. In CVPR, 2015.","title":"Discrete hyper-graph matching","authors":"Yan","year":"2015","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":78,"raw":"[Yan et al., 2016] J. Yan, M. Cho, H. Zha, X. Yang, and S. Chu. Multi-graph matching via affinity optimization with graduated consistency regularization. TPAMI, 2016.","title":"Multi-graph matching via affinity optimization with graduated consistency regularization","authors":"J Yan, M Cho, H Zha, X Yang, S Chu","year":"2016","venue":"TPAMI","doi":null,"url":null,"internal_url":null},{"position":79,"raw":"[Yu et al., 2018] T. Yu, J. Yan, J. Zhao, and B. Li. Joint cuts and matching of partitions in one graph. In CVPR, 2018.","title":"Joint cuts and matching of partitions in one graph","authors":"T Yu, J Yan, J Zhao, B Li","year":"2018","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":80,"raw":"[Yu et al., 2020] T. Yu, R. Wang, J. Yan, and B. Li. Learning deep graph matching with channel-independent embedding and hun- garian attention. In ICLR, 2020.","title":"Learning deep graph matching with channel-independent embedding and hungarian attention","authors":"T Yu, R Wang, J Yan, B Li","year":"2020","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":81,"raw":"[Zaheer et al., 2017] Manzil Zaheer, Satwik Kottur, Siamak Ravan- bakhsh, Barnabas Poczos, Ruslan R Salakhutdinov, and Alexan- der J Smola. Deep sets. In NIPS, 2017.","title":"Deep sets","authors":"Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan R Salakhutdinov, Alexander J Smola","year":"2017","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":82,"raw":"[Zanfir and Sminchisescu, 2018] A. Zanfir and C. Sminchisescu. Deep learning of graph matching. In CVPR, 2018.","title":"Deep learning of graph matching","authors":"A Zanfir, C Sminchisescu","year":"2018","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":83,"raw":"[Zass and Shashua, 2008] R. Zass and A. Shashua. Probabilistic graph and hypergraph matching. In CVPR, 2008.","title":"Probabilistic graph and hypergraph matching","authors":"R Zass, A Shashua","year":"2008","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":84,"raw":"[Zhang et al., 2015] Y. Zhang, J. Tang, and Z. Yang. Cosnet: Con- necting heterogeneous social networks with local and global con- sistency. In SIG KDD, 2015.","title":"Cosnet: Connecting heterogeneous social networks with local and global consistency","authors":"Y Zhang, J Tang, Z Yang","year":"2015","venue":"SIG KDD","doi":null,"url":null,"internal_url":null},{"position":85,"raw":"[Zhang et al., 2019] Zhen Zhang, Yijian Xiang, Lingfei Wu, Bing Xue, and Arye Nehorai. Kergm: Kernelized graph matching. In NIPS, pages 3330-3341, 2019.","title":"Kergm: Kernelized graph matching","authors":"Zhen Zhang, Yijian Xiang, Lingfei Wu, Bing Xue, Arye Nehorai","year":"2019","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":86,"raw":"[Zhou and Torre, 2016] F. Zhou and F. Torre. Factorized graph matching. IEEE PAMI, 2016.","title":"Factorized graph matching","authors":"F Zhou, F Torre","year":"2016","venue":"IEEE PAMI","doi":null,"url":null,"internal_url":null},{"position":87,"raw":"[Zhou et al., 2015] X. Zhou, M. Zhu, and K. Daniilidis. Multi- image matching via fast alternating minimization. In ICCV, 2015.","title":"Multiimage matching via fast alternating minimization","authors":"Zhou","year":"2015","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":88,"raw":"[Zhou et al., 2018] F. Zhou, L. Liu, K. Zhang, G. Trajcevski, J. Wu, and T. Zhong. Deeplink: A deep learning approach for user iden- tity linkage. In INFOCOM, 2018.","title":"Deeplink: A deep learning approach for user identity linkage","authors":"F Zhou, L Liu, K Zhang, G Trajcevski, J Wu, T Zhong","year":"2018","venue":"INFOCOM","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}